Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification

  • Authors:

  • Jakob Puchinger;Günther R. Raidl

  • Affiliations:

  • Institute of Computer Graphics and Algorithms, Vienna University of Technology, Vienna, Austria;Institute of Computer Graphics and Algorithms, Vienna University of Technology, Vienna, Austria

  • Venue:
  • IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
  • Year:
  • 2005

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Abstract

In this survey we discuss different state-of-the-art approaches of combining exact algorithms and metaheuristics to solve combinatorial optimization problems. Some of these hybrids mainly aim at providing optimal solutions in shorter time, while others primarily focus on getting better heuristic solutions. The two main categories in which we divide the approaches are collaborative versus integrative combinations. We further classify the different techniques in a hierarchical way. Altogether, the surveyed work on combinations of exact algorithms and metaheuristics documents the usefulness and strong potential of this research direction.